Accurate and repeatable quantitative analysis of tissue is important to characterize the progression of various pathologies, and to evaluate effects that new therapies might have. To date, little if any reliable structural information exists at the tissue level (1–1000 microns, that is, in the range microscopic to mesoscopic). It is believed that if reliable, multi-dimensional tissue structural information existed in readily accessible databases capable of continuous assimilation with newly acquired information, including clinical and molecular (including genetic) information, such information would serve to enhance and accelerate new advances in tissue engineering, drug design, gene discovery, proteomics, and genomics research.
The present invention overcomes the problems of the current art. Present visual/manual analysis of tissue is slow, difficult, and prone to error. The present invention eliminates manual zooming and panning at several resolution scales to establish relevant tissue features. Disclosed herein are image processing and analysis methods to automate feature extraction from tissue and to enable an objective, quantitative definition of tissue geometry. Measurement results are input into a relational database where they are statistically analyzed and compared across studies.
In particular, the present invention provides a capacity to visualize and quantitatively analyze different structural elements of a given tissue which are otherwise difficult to visualize, and quantitate accurately and efficiently. The present invention is also efficient over prior art known methods in that the time required for the geometrical analysis of a given tissue or organ is reduced by several fold. For example, one skilled in the art can accurately analyze 30–40 tissue specimens in about 3 hours by practicing the present invention.